The present invention relates to a computational framework that guides pathway modifications, through reaction additions and deletions.
The generation of bioconversion pathways has attracted significant interest in recent years. The first systematic effort towards this end was made by Seressiotis and Bailey (Seressiotis & Bailey, 1988), who utilized the concepts of Artificial Intelligence in developing their software. This was followed by a case study on the production of lysine from glucose and ammonia performed by Mavrovouniotis et al. (Mavrovouniotis et al., 1990) utilizing an algorithm based on satisfying the stoichiometric constraints on reactions and metabolites in an iterative fashion. More recently, elegant graph theoretic concepts (e.g., P-graphs (Fan et al., 2002) and k-shortest paths algorithm (Eppstein, 1994)) were pioneered to identify novel biotransformation pathways based on the tracing of atoms (Arita, 2000; Arita, 2004), enzyme function rules and thermodynamic feasibility constraints (Hatzimanikatis et al., 2003). Most of these approaches have been demonstrated by applying them on a relatively small database of reactions. Their performance on genome-scale databases of metabolic reactions, such as the KEGG database which consists of approximately 5000 reactions (Kanehisa et al., 2002), will dramatically suffer.
Very recently, a heuristic approach based on determining the minimum pathway cost (based on any biochemical property) was proposed (McShan et al., 2003). This approach is quite successful in delineating the pathways for conversion of one metabolite into another. However, like all other approaches discussed earlier, it fails to predict the yield of the product obtained by employing a specific pathway. Furthermore, these approaches mostly identify linear biotransformation pathways without ensuring the balanceability of all metabolites, especially the cofactors.
Therefore it is a primary object, feature, or advantage of the present invention to provide an optimization-based procedure which addresses the complexity associated with genome-scale networks.
It is a further object, feature, or advantage of the present invention to provide a method for constructing stoichiometrically-balanced bioconversion pathways, both branched and linear, that are efficient in terms of yield and the number of non-native reactions required in a host for product formation.
Another object, feature, or advantage of the present invention is to provide a method that enables the evaluation of multiple substrate choices.
Yet another object, feature, or advantage of the present invention is to provide a method for computationally suggesting the manner in which to achieve bioengineering objectives, including increased production objectives.
A further object, feature or advantage of the present invention is to determine candidates for gene deletion or addition through use of a model of a network of bioconversion pathways.
Yet another object, feature or advantage of the present invention is to provide an optimized method for computationally achieving a bioengineering objective that is flexible and robust.
A still further object, feature, or advantage of the present invention is to provide a method for computationally achieving a bioengineering objective that can take into account not only central metabolic pathways, but also other pathways such as amino acid biosynthetic and degradation pathways.
Yet another object, feature, or advantage of the present invention is to provide a method for computationally achieving a bioengineering objective that that can take into account transport rates, secretion pathways or other characteristics as optimization variables.
One or more of these and/or other objects, features and advantages of the present invention will become apparent after review of the following detailed description of the disclosed embodiments and the appended claims.